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Nanocluster-based ultralow-temperature driven oxide gate dielectrics for high-performance organic electronic devices
No full textThe development of novel dielectric materials with reliable dielectric properties and low-temperature processibility is crucial to manufacturing flexible and high-performance organic thin-film transistors (OTFTs) for next-generation roll-to-roll organic electronics. Here, we investigate the solution-based fabrication of high-k aluminum oxide (Al2O3) thin films for high-performance OTFTs. Nanocluster-based Al2O3 films fabricated by highly energetic photochemical activation, which allows low-temperature processing, are compared to the conventional nitrate-based Al2O3 films. A wide array of spectroscopic and surface analyses show that ultralow-temperature photochemical activation (6 MV/cm). Using this dielectric layer, precisely aligned microrod-shaped 2,7-dioctyl[1]benzothieno [3,2-b][1] benzothiophene (C8-BTBT) single-crystal OTFTs were fabricated via solvent vapor annealing and photochemical patterning of the sacrificial layer
A vulnerability-based approach to human-mobility reduction for countering COVID-19 transmission in London while considering local air quality
No full text© 2020 Elsevier B.V. An ecologic analysis was conducted to explore the correlation between air pollution, and COVID-19 cases and fatality rates in London. The analysis demonstrated a strong correlation (R2 > 0.7) between increment in air pollution and an increase in the risk of COVID-19 transmission within London boroughs. Particularly, strong correlations (R2 > 0.72) between the risk of COVID-19 fatality and nitrogen dioxide and particulate matter pollution concentrations were found. Although this study assumed the same level of air pollution across a particular London borough, it demonstrates the possibility to employ air pollution as an indicator to rapidly identify the city's vulnerable regions. Such an approach can inform the decisions to suspend or reduce the operation of different public transport modes within a city. The methodology and learnings from the study can thus aid in public transport's response to COVID-19 outbreak by adopting different levels of human-mobility reduction strategies based on the vulnerability of a given region
Additive batch electrospinning patterning of tethered gelatin hydrogel fibres with swelling-induced fibre curling
No full textLimited methods exist to integrate patterning of free-spanning hydrogel microfibres as part of an additive manufacturing strategy. We demonstrate 3D additive batch electrospinning (3D-abES) workflow to efficiently produce layered hydrogel gelatin microfibres, tethered to 3D printed thermoplastic structures of various shapes. Enabled by the digital-design approach, multiple 3D printed fibre devices can be fabricated in a batch efficiently with minimised sample-to-sample variance. Customised fibre patterns and device geometries can be rapidly altered to fit with potential applications in 6- to 24- well plate formats. The diameter of as-produced dry fibres is in a range of ∼2−4 μm. With the intended device applications in aqueous environments, we investigate the effect of combinations of processing parameters on the gelatin fibre integrity, and its swelling behaviour when immersed in water. Aided by the parametric study, patterns of swelling-induced fibre curling, from straight to wavy, can be tuned. Our findings could serve as a guide to optimise the 3D fabrication and patterning of electrospun hydrogel-like fibres made of crosslinked hydrophilic polymers and oligomers, for cell culture or bio-sensing applications
Learning hidden states in a chaotic system: A physics-informed echo state network approach
No full text© Springer Nature Switzerland AG 2020. We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained by using (i) data, which contains no information on the unmeasured state, and (ii) the physical equations of a prototypical chaotic dynamical system. Non-noisy and noisy datasets are considered. First, it is shown that the PI-ESN can accurately reconstruct the unmeasured state. Second, the reconstruction is shown to be robust with respect to noisy data, which means that the PI-ESN acts as a denoiser. This paper opens up new possibilities for leveraging the synergy between physical knowledge and machine learning to enhance the reconstruction and prediction of unmeasured states in chaotic dynamical systems
Learning ergodic averages in chaotic systems
No full text© Springer Nature Switzerland AG 2020. We propose a physics-informed machine learning method to predict the time average of a chaotic attractor. The method is based on the hybrid echo state network (hESN). We assume that the system is ergodic, so the time average is equal to the ergodic average. Compared to conventional echo state networks (ESN) (purely data-driven), the hESN uses additional information from an incomplete, or imperfect, physical model. We evaluate the performance of the hESN and compare it to that of an ESN. This approach is demonstrated on a chaotic time-delayed thermoacoustic system, where the inclusion of a physical model significantly improves the accuracy of the prediction, reducing the relative error from 48% to 1%. This improvement is obtained at the low extra cost of solving a small number of ordinary differential equations that contain physical information. This framework shows the potential of using machine learning techniques combined with prior physical knowledge to improve the prediction of time-averaged quantities in chaotic systems
Self-Selective Resistive Device with Hybrid Switching Mode for Passive Crossbar Memory Application
No full textIn this letter, we experimentally demonstrated a novel resistive device with a hybrid switching mode that can be alternated between volatile threshold switching and non-volatile resistive switching. The device consists of dual-functional layers VO2 /HfO2 sandwiched by symmetrical TiN electrodes. A >20 unified ratio for selectivity and memory window is obtained. Owing to the stable resistive behavior of HfO2 and insulator-metal transition of VO2, the device shows excellent uniform switching parameters in both switching modes with a high on-state current density (1E4 A/cm2) and fast switching/recovery speed (< 30 ns). This self-selective resistive memory is of great potential in the high-density crossbar array, particularly for the future 3D-Vertical resistive random access memory (RRAM) integration
Silicon Photonic 2.5D Multi-Chip Module Transceiver for High-Performance Data Centers
No full textWidespread adoption of silicon photonics into datacenters requires that the integration of the driving electronics with the photonics be an essential component of transceiver development. In this article, we describe our silicon photonic transceiver design: a 2.5D integrated multi-chip module (MCM) for 4-channel wavelength division multiplexed (WDM) microdisk modulation targeting 10 Gbps per channel. A silicon interposer is used to provide connectivity between the photonic integrated circuit (PIC) and the commercial transimpedance amplifiers (TIAs). Error free modulation is demonstrated at 10 Gbps with -16 dBm received power for the photonic bare die and at 6 Gbps with -15 dBm received power for the first iteration of the MCM transceiver. In this context, we outline the different integration approaches currently being employed to interface between electronics and photonics - monolithic, 2D, 3D, and 2.5D - and discuss their tradeoffs. Notable demonstrations of the various integration architectures are highlighted. Finally, we address the scalability of the architecture and highlight a subsequent prototype employing custom electronic integrated circuits (EICs)
Head-up display with dynamic depth-variable viewing effect
No full textHead-Up Displays (HUDs) can reduce duration and frequency of drivers looking away from traffic scenes, but information contents of different importance are usually displayed at the same time in contemporary HUD models. Such configurations increase the time that a driver searches for critical information and it is essential that the said information can quickly attract driver's attention without affecting his focus on the road. We introduce an alternative approach of displaying critical information with a variable depth in a designated local area of a HUD image. The variations are engineered to create a dynamic pop-up effect for hazard warnings, such as a car exceeding the speed-limit or approaching certain road signs. The image depth of the corresponding area is designed to vary by about half a metre and the image size by 1.4 times for a natural viewing experience, using an off-the-shelf liquid lens with electrically tuneable focus depths. The HUD optics are adjusted to have an extended eye-box to accommodate driver's head movement and a uniform image brightness across the eye-box
Multi-fidelity approach to Bayesian parameter estimation in subsurface heat and fluid transport models
No full textThe increased use of the urban subsurface for competing purposes, such as anthropogenic infrastructures and geothermal energy applications, leads to an urgent need for large-scale sophisticated modelling approaches for coupled mass and heat transfer. However, such models are subject to large uncertainties in model parameters, the physical model itself and in available measured data, which is often rare. Thus, the robustness and reliability of the computer model and its outcomes largely depend on successful parameter estimation and model calibration, which are hampered by the computational burden of large-scale coupled models. To tackle this problem, we develop a novel Bayesian approach for parameter estimation, which allows us to account for different sources of uncertainty, is capable of dealing with sparse field data and makes optimal use of the output data from expensive numerical model runs. This is achieved by combining output data from different models that represent the same physical problem, but at different levels of fidelity, e.g. reflected by different spatial resolution. By applying this new approach to a 1D analytical heat transfer model and a large-scale semi-3D numerical model while using synthetic data, we show that the accuracy and precision of parameter estimation by this multi-fidelity framework by far exceeds the standard single-fidelity results. The consideration of different error terms in the Bayesian framework also allows assessment of the model bias and the discrepancy between the different fidelity levels. These are emulated by Gaussian Process models, which facilitate re-iteration of the parameter estimation without additional model runs
Spherical indentation response of a Ni double gyroid nanolattice
No full textThe effect of indentation strain εi upon hardness H and elastic modulus E of a Ni Double Gyroid (DG) nanolattice was investigated using a spherically-tipped nanoindenter. H remains invariant, while E decreases linearly, with increasing εi. Results reveal the progressive collapse of the DG lattice beneath the indenter. The measured values of H and extrapolated value of E at εi = 0 were used to estimate the yield strength and elastic modulus of the Ni cell walls. The latter was compared with the ideal strength of Ni, nanocrystalline films and of sub-100 nm diameter single crystals